Medical imaging is generally recognised as key to better diagnosis and patient care. It has experienced explosive growth over the last few years due to imaging modalities such as x-ray, Computed Tomography (CT), ultrasound, Magnetic Resonance Imaging (MRI) and Positron Emission Tomography (PET). Conventionally, medical images have been inspected visually by highly-trained medical practitioners in order to identify anatomic structures of interest, such as lesions. However, the process can be tedious, time consuming and must be performed with great care.
Computed Tomography Colonography (CTC) is a form of medical imaging that has been used for the detection and screening of colonic cancers. The purpose of CTC is to locate and identify polyps on the colon wall by computationally processing CT scans. A polyp is an abnormal growth on a mucous membrane, and may be benign or malignant (cancerous). The effectivess of CTC is hindered by the time taken to interpret the large amount of data that is generated by a CTC scan, variability among human experts and the complexity of the classification. A typical modern CTC scan produces around one thousand axial CT images (slices) for the supine and prone data sets. To address these problems, a number of Computer Aided Detection (CAD) schemes for detecting polyps with CTC have been proposed.
A conventional CAD system for polyp detection comprises two steps. In the first step, which is known as “candidate generation”, image processing techniques are used in order to identify initial polyp candidates (where a “polyp candidate” is the term used to describe an anatomical structure that might be a polyp, but which has not yet been confirmed to be a polyp). Generally, a large number of false positives are produced by the first step and significantly outweigh the number of true positives. The term “false positive” (FP) refers to a polyp candidate that is not actually a polyp, whereas the term “true positive” (TP) refers to a polyp candidate that can be confirmed to be a polyp. Hence, the second step of a CAD system involves the use of image processing techniques to reduce the number of FPs, preferably without also reducing the number of TPs. This second step is known as “false positive reduction”.
Typical approaches to CTC CAD can be classified as “shape-based” or “appearance-based”.
Shape-based methods calculate certain characteristics of the geometry of structures in the medical image, so as to detect structures having shapes that are commonly associated with lesions. Known shape-based methods typically rely on various “shape features” derived from first order differential geometric quantities (such as surface normal or gradient) or second order Hessian matrices (such as principal curvature, mean curvature, Gaussian curvature, or shape index). Such shape-based methods are useful in detecting spherical objects or objects with local spherical elements. However, in practice, lesions (such as polyps) are often abnormal growths that exhibit varying morphology, and many polyps can not be adequately characterised using local differential geometric measures. Hence, shape-based methods may fail to detect lesions with sufficient reliability.
Appearance-based methods typically rely on non-geometric features derived from the image intensity, such as wavelet features. While potentially useful in detecting polyps of wider shape morphologies, appearance-based methods originate from research into face detection and so are not optimal for detecting lesions. Appearance-based methods may fail to detect lesions with sufficient reliability.
Thus, there is a need for improved methods for computer aided detection of lesions, abnormalities or other anatomical structures in medical images, particularly for use in CTC.
P. R. S. Mendonça et al., “Detection of Polyps via Shape and Appearance Modeling” (Proc. MICCAI 2008 Workshop: Computational and Visualization Challenges in the New Era of Virtual Colonoscopy, pp. 33-39) relates to a CAD system for the detection of colorectal polyps in CT. The CAD system is based on shape and appearance modelling of structures of the colon and rectum.